Exploring the Potential of a Global Emerging Contaminant Early Warning Network through the Use of Retrospective Suspect Screening with High-Resolution Mass Spectrometry

Fragmentation Pattern-Based Screening Strategy Combining Diagnostic Ion and Neutral Loss Uncovered Novel para-Phenylenediamine Quinone Contaminants in the Environment

Identifying transformed emerging contaminants in complex environmental compartments is a challenging but meaningful task. Substituted para-phenylenediamine quinones (PPD-quinones) are emerging contaminants originating from rubber antioxidants and have been proven to be toxic to the aquatic species, especially salmonids. The emergence of multiple PPD-quinones in various environmental matrices and evidence of their specific hazards underscore the need to understand their environmental occurrences. Here, we introduce a fragmentation pattern-based nontargeted screening strategy combining full MS/All ion fragmentation/neutral loss-ddMS2 scans to identify potential unknown PPD-quinones in different environmental matrices. Using diagnostic fragments of m/z 170.0600, 139.0502, and characteristic neutral losses of 199.0633, 138.0429 Da, six known and three novel PPD-quinones were recognized in air particulates, surface soil, and tire tissue. Their specific structures were confirmed, and their environmental concentration and composition profiles were clarified with self-synthesized standards. N-(1-methylheptyl)-N'-phenyl-1,4-benzenediamine quinone (8PPD-Q) and N,N'-di(1,3-dimethylbutyl)-p-phenylenediamine quinone (66PD-Q) were identified and quantified for the first time, with their median concentrations found to be 0.02-0.21 μg·g-1 in tire tissue, 0.40-2.76 pg·m-3 in air particles, and 0.23-1.02 ng·g-1 in surface soil. This work provides new evidence for the presence of unknown PPD-quinones in the environment, showcasing a potential strategy for screening emerging transformed contaminants in the environment.

Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment

The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives

This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS's role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.

Influence of extraction windows for data-independent acquisition on feature annotation during suspect screening

Non-target analysis (NTA) using high-resolution mass spectrometry is becoming a useful approach to screen for suspect and unknown chemicals. For comprehensive analyses, data-independent acquisition (DIA), like Sequential Windowed Acquisition of all THeoretical Mass Spectra (SWATH-MS) on Sciex instruments, is necessary, usually followed by library matching for feature annotation. The choice of parameters, such as acquisition window number and size, may influence the comprehensiveness of the suspect features detected. The goal of this study was to assess how mass spectrometric DIA settings may influence the ability to obtain confident annotations and identifications of features in environmental (river water, passive sample extract (PSE)), wastewater (unpreserved and acidified) and biological (urine) sample matrices. Each matrix was analysed using 11 different MS methods, with 5-15 variable size acquisition windows. True positive (TP) annotation (i.e., matching experimental and library spectra) rates were constant for PSE (40%) and highest for urine (18%), wastewater (34% and 36%, unpreserved and acidified, respectively) and river water (8%) when using higher numbers of windows (15). The number of annotated features was highest for PSE (12%) and urine (8.5%) when using more acquisition windows (9 and 14, respectively). Less complex matrices (based on average total ion chromatogram intensities) like river water, unpreserved and acidified wastewater have higher annotation rates (7.5%, 8% and 13.2%, respectively) when using less acquisition windows (5-6), indicating matrix dependency of optimum settings. Library scores varied widely for correct (scores between 6 and 100) as well as incorrect annotations (scores between 2 and 100), making it hard to define specific ideal cut-off values. Results highlight the need for properly curated libraries and careful optimization of SWATH-MS and other DIA methods for each individual matrix, finding the best ratio of total annotations to true positive, (i.e., correct) annotations to achieve best NTA results.

Assessment for the data processing performance of non-target screening analysis based on high-resolution mass spectrometry

Non-target screening (NTS) based on high-resolution mass spectrometry (HRMS) is considered one of the most comprehensive approaches for the characterization of contaminants of emerging concern (CECs) in a complex sample. This study evaluated the performance of NTS in aquatic environments (including peak picking, database matching, product identification, semi-quantification, etc.) based on a self-developed data processing method using 38 glucocorticoids as testing compounds. Data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes were used for obtaining the MS2 information for in-house or online database matching. Results indicate that DDA and DIA mode have their own advantages and can complement each other. The quantification method based on LC-HRMS has shown the potential to provide a fast and acceptable result for testing compounds. Finally, a matrix spike analysis was carried out on 66 CECs across different usage categories in wastewater, surface water, and seawater matrix samples, together with a case study performed for characterizing the whole contaminants in a Pearl River sample, to better illustrate the application potential of NTS workflow and the credibility of NTS outcomes. This study provides a foundation for novel applications of HRMS data by NTS workflow to identify and quantify CECs in complex systems.

Identification of polar organic chemicals in the aquatic foodweb: Combining high-resolution mass spectrometry and trend analysis

Environmental risk assessment of chemical contaminants requires prioritizing of substances taken up by biota as it is a starting point for potential adverse effects. Although knowledge about the occurrence of known chemical pollutants in aquatic organisms has significantly improved during the last decade, there is still a poor understanding for a broad range of more polar compounds. To tackle this issue, we proposed an approach that identifies bioaccumulative and biomagnifiable polar chemicals using liquid chromatography coupled with electrospray ionization to high resolution tandem mass spectrometry (LC-HRMS/MS) and combine it with trend analysis using hierarchical clustering. As a proof-of-concept, this approach was implemented on various organisms and compartments (sediment, litter leaves, periphytic biofilm, invertebrates and fish) collected from a small urban river. HRMS/MS data measured via data-independent acquisition mode were retrospectively analysed using two analytical strategies: (1) retrospective target and (2) suspect/non-target screening. In the retrospective target analysis, 56 of 361 substances spanning a broad range of contaminant classes were detected (i.e. 26 in fish, 18 in macroinvertebrates, 28 in leaves, 29 in periphyton and 32 in sediments, with only 7 common to all compartments), among which 49 could be quantified using reference standards. The suspect screening approach based on two suspect lists (in-house, Norman SusDat) led to the confirmation of 5 compounds with standards (three xenobiotics at level 1 and two lipids at level 2) and tentative identification of seven industrial or natural chemicals at level 2 and 3 through a mass spectra library match. Overall, this proof-of-concept study provided a more comprehensive picture of the exposure of biota to emerging contaminants (i.e., the internal chemical exposome) and potential bioaccumulation or biomagnification of polar compounds along the trophic chain.

What is in the fish? Collaborative trial in suspect and non-target screening of organic micropollutants using LC- and GC-HRMS

A collaborative trial involving 16 participants from nine European countries was conducted within the NORMAN network in efforts to harmonise suspect and non-target screening of environmental contaminants in whole fish samples of bream (Abramis brama). Participants were provided with freeze-dried, homogenised fish samples from a contaminated and a reference site, extracts (spiked and non-spiked) and reference sample preparation protocols for liquid chromatography (LC) and gas chromatography (GC) coupled to high resolution mass spectrometry (HRMS). Participants extracted fish samples using their in-house sample preparation method and/or the protocol provided. Participants correctly identified 9-69 % of spiked compounds using LC-HRMS and 20-60 % of spiked compounds using GC-HRMS. From the contaminated site, suspect screening with participants' own suspect lists led to putative identification of on average ∼145 and ∼20 unique features per participant using LC-HRMS and GC-HRMS, respectively, while non-target screening identified on average ∼42 and ∼56 unique features per participant using LC-HRMS and GC-HRMS, respectively. Within the same sub-group of sample preparation method, only a few features were identified by at least two participants in suspect screening (16 features using LC-HRMS, 0 features using GC-HRMS) and non-target screening (0 features using LC-HRMS, 2 features using GC-HRMS). The compounds identified had log octanol/water partition coefficient (KOW) values from -9.9 to 16 and mass-to-charge ratios (m/z) of 68 to 761 (LC-HRMS and GC-HRMS). A significant linear trend was found between log KOW and m/z for the GC-HRMS data. Overall, these findings indicate that differences in screening results are mainly due to the data analysis workflows used by different participants. Further work is needed to harmonise the results obtained when applying suspect and non-target screening approaches to environmental biota samples.

Nontarget Insights into the Fate of Cl-/Br-Containing DOM in Leachate during Membrane Treatment

Halogenated organic compounds in wastewater are persistent and bioaccumulative contaminants of great concern, but few are known at the molecular level. Herein, we focus on nontarget screening of halogenated dissolved organic matter (DOM) in highly concentrated organic matrices of waste leachates and their concentrates. Solid-phase extraction (SPE) was optimized before capturing halogenated signatures via HaloSeeker 2.0 software on mining full-scan high-resolution mass spectrometry (HRMS) fingerprints. This study identified 438 Cl-/Br-containing DOM formulas in 21 leachates and membrane concentrates. Among them, 334 formulas were achieved via SPE with mixed-sorbent cartridges (mixed-SPE), surpassing the 164 formulas achieved through Bond Elut PPL cartridges (PPL-SPE). Herein, only four samples identified via PPL-SPE exhibited a resolution of >50% for extracted Cl-/Br-containing DOM by either SPE. The halogenated DOM constituted 6.87% of the total DOM mass features. Nevertheless, more abundant adsorbable organic halogens deciphered waste leachates and highly concentrated waste streams as reservoirs for halogenated contaminants. Remarkably, 75.7-98.1% of Cl-/Br-containing DOM in primary membrane concentrates remained stable through the secondary membrane treatment, indicating the persistence of these unknown contaminants even post-treatment.

Critical Assessment of the Chemical Space Covered by LC-HRMS Non-Targeted Analysis

Non-targeted analysis (NTA) has emerged as a valuable approach for the comprehensive monitoring of chemicals of emerging concern (CECs) in the exposome. The NTA approach can theoretically identify compounds with diverse physicochemical properties and sources. Even though they are generic and have a wide scope, non-targeted analysis methods have been shown to have limitations in terms of their coverage of the chemical space, as the number of identified chemicals in each sample is very low (e.g., ≤5%). Investigating the chemical space that is covered by each NTA assay is crucial for understanding the limitations and challenges associated with the workflow, from the experimental methods to the data acquisition and data processing techniques. In this review, we examined recent NTA studies published between 2017 and 2023 that employed liquid chromatography-high-resolution mass spectrometry. The parameters used in each study were documented, and the reported chemicals at confidence levels 1 and 2 were retrieved. The chosen experimental setups and the quality of the reporting were critically evaluated and discussed. Our findings reveal that only around 2% of the estimated chemical space was covered by the NTA studies investigated for this review. Little to no trend was found between the experimental setup and the observed coverage due to the generic and wide scope of the NTA studies. The limited coverage of the chemical space by the reviewed NTA studies highlights the necessity for a more comprehensive approach in the experimental and data processing setups in order to enable the exploration of a broader range of chemical space, with the ultimate goal of protecting human and environmental health. Recommendations for further exploring a wider range of the chemical space are given.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Assessment of wastewater-borne pharmaceuticals in tissues and body fluids from riverine fish

Riverine fish in densely populated areas is constantly exposed to wastewater-borne contaminants from effluent discharges. These can enter the organism through the skin, gills or by ingestion. Whereas most studies assessing the contaminant burden in exposed fish have focused either on muscle or a limited set of tissues. Here we set out to generate a more comprehensive overview of the distribution of pollutants across tissues by analyzing a panel of matrices including liver, kidney, skin, brain, muscle, heart, plasma and bile. To achieve a broad analyte coverage with a minimal bias towards a specific contaminant class, sample extracts from four fish species were analyzed by High-Performance Liquid Chromatography (HPLC) - high-resolution mass spectrometry (HRMS) for the presence of 600 wastewater-borne pharmaceutically active compounds (PhACs) with known environmental relevance in river water through a suspect-screening analysis. A total of 30 compounds were detected by suspect screening in at least one of the analyzed tissues with a clear prevalence of antidepressants. Of these, 15 were detected at confidence level 2.a (Schymanski scale), and 15 were detected at confidence level 1 following confirmation with authentic standards, which furthermore enabled their quantification. The detected PhACs confirmed with level 1 of confidence included acridone, acetaminophen, caffeine, clarithromycin, codeine, diazepam, diltiazem, fluoxetine, ketoprofen, loratadine, metoprolol, sertraline, sotalol, trimethoprim, and venlafaxine. Among these substances, sertraline stood out as it displayed the highest detection frequency. The values of tissue partition coefficients for sertraline in the liver, kidney, brain and muscle were correlated with its physicochemical properties. Based on inter-matrix comparison of detection frequencies, liver, kidney, skin and heart should be included in the biomonitoring studies of PhACs in riverine fish.

InSpectra - A platform for identifying emerging chemical threats

Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate advancements in data processing. Here we present InSpectra, a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats. InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.

Stochastic Dynamic Mass Spectrometric Quantitative and Structural Analyses of Pharmaceutics and Biocides in Biota and Sewage Sludge

Mass spectrometric innovations in analytical instrumentation tend to be accompanied by the development of a data-processing methodology, expecting to gain molecular-level insights into real-life objects. Qualitative and semi-quantitative methods have been replaced routinely by precise, accurate, selective, and sensitive quantitative ones. Currently, mass spectrometric 3D molecular structural methods are attractive. As an attempt to establish a reliable link between quantitative and 3D structural analyses, there has been developed an innovative formula [DSD″,tot=∑inDSD″,i=∑in2.6388.10−17×Ii2¯−Ii¯2] capable of the exact determination of the analyte amount and its 3D structure. It processed, herein, ultra-high resolution mass spectrometric variables of paracetamol, atenolol, propranolol, and benzalkonium chlorides in biota, using mussel tissue and sewage sludge. Quantum chemistry and chemometrics were also used. Results: Data on mixtures of antibiotics and surfactants in biota and the linear dynamic range of concentrations 2–80 ng.(mL)−1 and collision energy CE = 5–60 V are provided. Quantitative analysis of surfactants in biota via calibration equation ln[D″SD] = f(conc.) yields the exact parameter |r| = 0.99991, examining the peaks of BAC-C12 at m/z 212.209 ± 0.1 and 211.75 ± 0.15 for tautomers of fragmentation ions. Exact parameter |r| = 1 has been obtained, correlating the theory and experiments in determining the 3D molecular structures of ions of paracetamol at m/z 152, 158, 174, 301, and 325 in biota.

Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data

Non-target analysis combined with liquid chromatography high resolution mass spectrometry is considered one of the most comprehensive strategies for the detection and identification of known and unknown chemicals in complex samples. However, many compounds remain unidentified due to data complexity and limited number structures in chemical databases. In this work, we have developed and validated a novel machine learning algorithm to predict the retention index (r[Formula: see text]) values for structurally (un)known chemicals based on their measured fragmentation pattern. The developed model, for the first time, enabled the predication of r[Formula: see text] values without the need for the exact structure of the chemicals, with an [Formula: see text] of 0.91 and 0.77 and root mean squared error (RMSE) of 47 and 67 r[Formula: see text] units for the NORMAN ([Formula: see text]) and amide ([Formula: see text]) test sets, respectively. This fragment based model showed comparable accuracy in r[Formula: see text] prediction compared to conventional descriptor-based models that rely on known chemical structure, which obtained an [Formula: see text] of 0.85 with an RMSE of 67.

Identification and trend analysis of organic cationic contaminants via non-target screening in suspended particulate matter of the German rivers Rhine and Saar

Non-target screening of suspended particulate matter (SPM), collected from the German rivers Rhine and Saar, was conducted with the goal of identifying organic, permanent cationic contaminants and of estimating their temporal trends over an extended period. Therefore, annual composite samples of SPM, provided by the German Environmental Specimen Bank, were extracted and analyzed with high resolution LC-QToF-MS/MS. To facilitate the identification of substances belonging to the class "permanent cations", prioritization methods were applied utilizing the physicochemical properties of these compounds. These methods include both interactions of the analyte molecules with cation exchange resins and analyzing mass deviations when changing from non-deuterated to deuterated mobile phase solvents during LC-MS analysis. By applying both methods in a combined approach, 123 of the initially detected 2695 features were prioritized, corresponding to a 95% data reduction. This led to the identification of 22 permanent cationic species. The organic dyes Basic Yellow 28 and Fluorescent Brightener 363 as well as two quaternary ammonium compounds (QACs) were detected in environmental samples for the first time to best of or knowledge. The other compounds include additional QACs, as well as quaternary tri-phenylphosphonium compounds (QPC/TPP). In addition to identification, we determined temporal trends of all compounds over a period of 13 years and assessed their ecotoxicological relevance based on estimated concentrations. The two QACs oleyltrimethylammonium and eicosyltrimethylammonium show significant increasing trends in the Rhine SPM and maximum concentrations in the Saar SPM of about 900 and 1400 µg/kg, respectively. In the case of the dyes, constant trends have been observed at the end of the studied period, but also maximum concentrations of 400 µg/kg for Basic Yellow 28 in 2006 and 1000 µg/kg for Fluorescent Brightener 363 in 2015, potentially indicating a strong ecotoxicological risk.

Suspect screening strategy for pesticide application history based on characteristics of trace metabolites

Pesticides are widely used to protect crops but can also threaten public health as they can remain in the environment for a long time. Additionally, some transformation products (TPs) of unknown toxicity, stability, or bioaccumulation properties can further be formed from the hydrolysis, photolysis and biodegradation of pesticides. The identification and quantification of those TPs can be challenging for environmental regulation and risk assessment due to a limited understanding about them. In this study, a suspect screening strategy for pesticide application history was developed and then used to organic products (tea). Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) was used to screen and identify the TPs in crops and their toxicity was subsequently predicted with the open-source software (ECOSAR and admetSAR). Finally, the SIRIUS software was applied and 142 TPs from 20 pesticides were identified in tea plants based on the fragmentation-degradation relationship. Of these, standards (level 1) and 53 were considered as tentatively identified (levels 2a and 2b). Some TPs were also found to be present in tea plants and soil after 65 days, thus indicating higher persistency or stability than parent pesticides. While others from diafenthiuron and neonicotinoids had higher predicted toxicity of daphnid, and demonstrated positive for honeybee toxicity. Suspect screening is a powerful tool to screen pesticide TPs on the complex matrix of crops. Such screening can provide potential evidence of pesticide application, especially in cases of illegal practices in organic farming.

Substance flow analysis on the leachate DOM molecules along five typical membrane advanced treatment processes

The processes combining biological treatment with membrane separation technologies have been widely adopted for leachate treatment. However, dissolved organic matter (DOM) of leachate membrane concentrates generated from various membrane separation technologies has not been systematically investigated in field scale. Therefore, substance flow analysis based on DOM molecular information of leachate membrane concentrates from primary membrane systems (i.e. nanofiltration (NF) and reverse osmosis (RO)) and secondary membrane systems (i.e. disk-tube reverse osmosis (DTRO) and humic substance filtration system (HSF)) in five engineering-scale leachate treatment facilities, obtained via ultra-performance liquid chromatography coupled with hybrid quadrupole Orbitrap mass spectrometry, was given and simultaneously compared. In NF concentrates (NFC), 45.1-98.5% of DOM originated from raw leachate (L-DOM) was concentrated, showing poor biodegradability. The L-DOM interception characteristics of NFC-fed HSF were mainly based on volume reduction but concentration effect. L-DOM in RO concentrates (ROC) showed a higher proportion of peak intensity reduced components, accounting for 50.3-96.8%, and organic composition changes were more dependent on water quality characteristics than membrane types. ROC-fed DTRO intercepted 49.3-72.6% of L-DOM, but DTRO may be less effective at intercepting DOM molecules in landfill leachate with higher oxidation levels. Considering risks from feasible treatment technologies, the difficulty for the treatment of leachate membrane concentrates followed the order of DTRO concentrates > ROC > NFC. This study suggests that ROC-fed DTRO need to be controlled to avoid amplifying the treatment difficulty. Besides, treatment technologies for RO and DTRO concentrates with low-concentrated but refractory DOM and high salts should be explored.

Multiclass target analysis of contaminants of emerging concern including transformation products, soil bioavailability assessment and retrospective screening as tools to evaluate risks associated with reclaimed water reuse

The occurrence of 200 multiclass contaminants of emerging concern (CECs) encompassing 168 medicinal products and transformation products (TPs), 5 artificial sweeteners, 12 industrial chemicals, and 15 other compounds was investigated in influent and effluent wastewater samples collected during 7 consecutive days from 5 wastewater treatment plants (WWTPs) located in Cyprus. The methodology included a generic solid-phase extraction protocol using mixed-bed cartridges followed by Ultra-High Performance Liquid Chromatography coupled with Quadrupole-Time of Flight Mass Spectrometry (UHPLC-QTOF-MS) analysis. A total of 63 CECs were detected at least in one sample, with 52 and 55 out of the 200 compounds detected in influents and effluents, respectively. Ten (10) out of the 24 families of parent compounds and associated TPs were found in the wastewater samples (influent or effluent). 1-H-benzotriazole, carbamazepine, citalopram, lamotrigine, sucralose, tramadol, and venlafaxine (>80 % frequency of appearance in effluents) were assessed with respect to their bioavailability in soil as part of different scenarios of irrigation with reclaimed water following a qualitative approach. A high score of 12 (high probability) was predicted for 2 scenarios, a low score of 3 (rare occasions) for 2 scenarios, while the rest 28 scenarios had scores 5-8 (unlikely or limited possibility) and 9-11 (possibly). Retrospective screening was performed with the use of a target database of 2466 compounds and led to the detection of 158 additional compounds (medicinal products (65), medicinal products TPs (15), illicit drugs (7), illicit drugs TPs (3), industrial chemicals (11), plant protection products (25), plant protection products TPs (10), and various other compounds (22). This work aspires to showcase how the presence of CECs in wastewater could be investigated and assessed at WWTP level, including an expert-based methodology for assessing the soil bioavailability of CECs, with the aim to develop sustainable practices and enhance reclaimed water reuse.

European scale assessment of the potential of ozonation and activated carbon treatment to reduce micropollutant emissions with wastewater

Micropollutants (MPs) in wastewater pose a growing concern for their potential adverse effects on the receiving aquatic environment, and some countries have started requiring that wastewater treatment plants remove them to a certain extent. Broad spectrum advanced treatment processes, such as ozonation, activated carbon or their combination, are expected to yield a significant reduction in the toxicity of effluents. Here we quantify the reduction of effluent toxicity potentially achieved by implementing these advanced treatment solutions in a selection of European wastewater treatment plants. To this end, we refer to a list of "total pollution proxy substances" (TPPS) composed of 1337 chemicals commonly found in wastewater effluents according to a compilation of datasets of measured concentrations. We consider these substances as an approximation of the "chemical universe" impinging on the European wastewater system. We evaluate the fate of the TPPS in conventional and advanced treatment plants using a compilation of experimental physicochemical properties that describe their sorption, volatilization and biodegradation during activated sludge treatment, as well as known removal efficiency in ozonation and activated carbon treatment, while filling the gaps through in silico prediction models. We estimate that the discharge of micropollutants with wastewater effluents in the European Union has a cumulative MP toxicity to the environment equal to the discharge of untreated wastewater of ca. 160 million population equivalents (PE), i.e. about 30 % of the generated wastewater in the EU. If all plants above a capacity of 100,000 PE were equipped with advanced treatment, we show that this load would be reduced to about 95 million PE. In addition, implementing advanced treatment in wastewater plants above 10,000 PE discharging to water bodies with an average dilution ratio smaller than 10 would yield a widespread improvement in terms of exposure of freshwater ecosystems to micropollutants, almost halving the part of the stream network exposed to the highest toxic risks. Our analysis provides background for a cost-effectiveness appraisal of advanced treatment "at the end of the pipe", which could lead to optimized interventions. This should not be regarded as a stand-alone solution, but as a complement to policies for the control of emissions at the source for the most problematic MPs.

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background

The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.

Results

The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).

Conclusions

The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12302-022-00680-6.

A Novel 4-Set Venn Diagram Model Based on High-Resolution Mass Spectrometry To Monitor Wastewater Treatment

A 4-set Venn diagram model oriented to high-resolution mass spectrometry (HRMS) data was developed to decipher the fate of dissolved organic matters (DOM) in three-stage continuous wastewater treatment processes. In total, 24 typical wastewater treatment modes conceptualized into a combination of three stages were generalized so that this model can be applied to all common types of actual wastewater treatment processes. As a result, eight kinds of native DOM and seven kinds of wastewater-produced (WW-produced) DOM separately represented by each proper subset of the 4-set Venn diagram could be identified so as to offer a molecular profile of DOM transformation. The 15 proper subsets of the 4-set Venn diagram could then explain how different wastewater treatment units work. Transformation rates of each DOM molecular formula can be estimated as a semiquantitative result. We further discussed the relationship between the transformation rates and proper subsets. As a proof of concept, the 4-set Venn diagram model was successfully applied in a complicated full-scale mature leachate treatment process with nine treatment units. This model can help to overcome the challenging task of data mining when applying HRMS and reduce the workload of data screening in the subsequent structural annotation.

Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning

High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification remains challenging due to the lack of orthogonal sources of information (e.g., unique fragments). Collision cross section (CCS) values measured by ion mobility spectrometry (IMS) offer an additional identification dimension to increase the confidence level. Thanks to the advances in analytical instrumentation, an increasing application of IMS hybrid with high-resolution mass spectrometry (HRMS) in NTS has been reported in the recent decades. Several CCS prediction tools have been developed. However, limited CCS prediction methods were based on a large scale of chemical classes and cross-platform CCS measurements. We successfully developed two prediction models using a random forest machine learning algorithm. One of the approaches was based on chemicals’ super classes; the other model was direct CCS prediction using molecular fingerprint. Over 13,324 CCS values from six different laboratories and PubChem using a variety of ion-mobility separation techniques were used for training and testing the models. The test accuracy for all the prediction models was over 0.85, and the median of relative residual was around 2.2%. The models can be applied to different IMS platforms to eliminate false positives in small molecule identification.

A Framework for Utilizing High-Resolution Mass Spectrometry and Nontargeted Analysis in Rapid Response and Emergency Situations

Unknown chemical releases constitute a large portion of the rapid response situations to which the US Environmental Protection Agency is called on to respond. Workflows used to address unknown chemical releases currently involve screening for a large array of known compounds using many different targeted methods. When matches are not found, expert analytical chemistry knowledge is used to propose possible candidates from the available data, which generally includes low-resolution mass spectra and situational clues such as the location of the release, nearby industrial operations, and other field-reported facts. The past decade has witnessed dramatic improvements in capabilities for identifying unknown compounds using high-resolution mass spectrometry (HRMS) and nontargeted analysis (NTA) approaches. Complementary developments in cheminformatics tools have further enabled an increase in NTA throughput and identification confidence. Together with the expanding availability of HRMS instrumentation in monitoring laboratories, these advancements make NTA highly relevant to rapid response scenarios. In this article, we introduce the concept of NTA as it relates to rapid response needs and describe how it can be applied to address unknown chemical releases. We advocate for the consideration of HRMS-based NTA approaches to support future rapid response scenarios. Environ Toxicol Chem 2022;41:1117-1130. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.

Application of a novel prioritisation strategy using non-target screening for evaluation of temporal trends (1969-2017) of contaminants of emerging concern (CECs) in archived lynx muscle tissue samples

Most environmental monitoring studies of contaminants of emerging concern (CECs) focus on aquatic species and target specific classes of CECs. Even with wide-scope target screening methods, relevant CECs may be missed. In this study, non-target screening (NTS) was used for tentative identification of potential CECs in muscle tissue of the terrestrial top predator Eurasian lynx (Lynx lynx). Temporal trend analysis was applied as a prioritisation tool for archived samples, using univariate statistical tests (Mann-Kendall and Spearman rank). Pooled lynx muscle tissue collected from 1969 to 2017 was analysed with an eight-point time series using a previously validated screening workflow. Following peak detection, peak alignment, and blank subtraction, 12,941 features were considered for statistical analysis. Prioritisation by time-trend analysis detected 104 and 61 features with statistically significant increasing and decreasing trends, respectively. Following probable molecular formula assignment and elucidation with MetFrag, two compounds with increasing trends, and one with a decreasing trend, were tentatively identified. These results show that, despite low expected concentration levels and high matrix effects in terrestrial species, it is possible to prioritise CECs in archived lynx samples using NTS and univariate statistical approaches.

TrendProbe: Time profile analysis of emerging contaminants by LC-HRMS non-target screening and deep learning convolutional neural network

Peak prioritization is one of the key steps in non-target screening of environmental samples to direct the identification efforts to relevant and important features. Occurrence of chemicals is sometimes a function of time and their presence in consecutive days (trend) reveals important aspects such as discharges from agricultural, industrial or domestic activities. This study presents a validated computational framework based on deep learning conventional neural network to classify trends of chemicals over 30 consecutive days of sampling in two sampling sites (upstream and downstream of a river). From trend analysis and factor analysis, the chemicals could be classified into periodic, spill, increasing, decreasing and false trend. The developed method was validated with list of 42 reference standards (target screening) and applied to samples. 25 compounds were selected by the deep learning and identified via non-target screening. Three classes of surfactants were identified for the first time in river water and two of them were never reported in the literature. Overall, 21 new homologous series of the newly identified surfactants were tentatively identified. The aquatic toxicity of the identified compounds was estimated by in silico tools and a few compounds along with their homologous series showed potential risk to aquatic environment.

Suspecting screening "known unknown" pesticides and transformation products in soil at pesticide manufacturing sites

The occurrence and risks of pesticides and their transformation products in soil at the manufacturing sites are "known unknowns." In this study, pesticides and their transformation products were screened in soil at 6 pesticide manufacturing sites across China using liquid and gas chromatography coupled with quadrupole time-of-flight mass spectrometry. The screening strategy can correctly identify 75% of 209 pesticides spiked at 50 ng g^-1. A total of 212 pesticides were identified; 23.1% of pesticides detected were above 200 ng g^-1, and the maximum concentration was 1.5 × 10⁵ ng g^-1. The risk quotients of 20% pesticides were greater than 1, and the maximum risk quotient of imidacloprid reached 6.3 × 10⁴. The most recent site showed a larger number of pesticides with higher diversity, whereas older sites were dominated by organochlorine insecticides. The extended screen identified 163 transformation products with concentrations up to 6.6 × 10⁴ ng g^-1. Half of the transformation products had higher concentrations than their parent compounds, and metabolic ratios up to 371 were observed. The results of this study validate the prevalence of pesticides and their transformation products in soil at pesticide manufacturing sites. The results also highlight the importance of comprehensive screening at industrial sites and call for improved management and regulation of pesticide manufacturing, particularly for in-service facilities.

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data

Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.

Identifying active xenobiotics in humans by use of a suspect screening technique coupled with lipidomic analysis

Lipidomic analysis has been proven to be a powerful technique to explore the underlying associations between xenobiotics and health status of organisms. Here, we established a strategy that combined the lipidomic analysis with high-throughput suspect contaminant screening technique with an aim to efficiently identify active xenobiotics in humans. Firstly, in the light of single liquid phase equilibrium of chloroform-methanol-water (15:14:2, v/v/v), we developed an efficient method that was able to simultaneously extract both polar and nonpolar lipids in serum samples. By use of this method, targeted and non-targeted lipid analyses were conducted for n = 120 serum samples collected from Wuxi city, China. Secondly, we established a suspect database containing 1450 contaminants that have been previously reported in human samples, and contaminants in this database were screened in the same batch of serum samples by use of high-resolution mass spectrometry (HR-MS). Thirdly, the underlying associations between suspect contaminants and lipids were explored and discussed, and we observed that levels of some lipids were statistically correlated with concentrations of numerous contaminants. Among these active contaminants, 23 ones were identified on the basis of HR MS¹ and MS² characteristics, and these contaminants belonged to the classes of phthalates, phenols, parabens, or perfluorinated compounds (PFCs). Three active xenobiotics were fully validated by comparison with authentic standards, and they were perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and diethyl phthalate (DEP). There were statistically significant changes in levels of triglyceride (TG), lysophosphocholine (LPC), and sphingomyelin (SM) as peak areas of xenobiotics increase. We also observed that, among target lipid molecules, 18:0 lysophosphatidylethanolamine (LPE(18:0)) was very sensitive, and this lipid responded to exposure of various contaminants. Our present study provides novel knowledge on potential alteration of lipid metabolism in humans following exposure to xenobiotics, and provides an efficient strategy for efficiently identifying active xenobiotics in humans.

Patterns of pharmaceuticals use during the first wave of COVID-19 pandemic in Athens, Greece as revealed by wastewater-based epidemiology

Since 2019, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), impaired public health with considerable morbidity and mortality due to the lack of vaccines and effective treatment. The severe disease mainly harmed adults with predisposing medical comorbidities (such as heart disease, hypertension, chronic lung disease), while it can occur in healthy individuals that may be asymptomatic. Wastewater-based Epidemiology (WBE), a non-invasive, objective, chemical tool was used to monitor and estimate the changes in drug's consumption and prescription patterns under normal conditions (2019) and under COVID-19 pandemic conditions (2020). NSAIDs, antihypertensives, diuretics, antiepileptics, antilipidemics, antibiotics, analgesics, antivirals, anticancer drugs, contrast iodinated drugs, antidiabetics, antiallergic drugs, antiulcers and other pharmaceuticals were studied in wastewater and revealed the application of various treatments during the first wave of the pandemic in Athens, Greece. Data were correlated with COVID-19 infection therapeutical plans. The result of the analysis revealed a remarkable increase for antiviral drugs (170%), hydroxychloroquine (387%), and antibiotics (57%), which were the most applied treatments against COVID-19 during the first wave in Greece. In agreement with related authorities urge, NSAIDs presented decrease (27%) during the first lockdown, while paracetamol demonstrated a remarkable increase (198%). The use levels for Angiotensin II receptor blockers such as valsartan, and co-administrated diuretics, such as hydrochlorothiazide, were reduced during 2020, by 32% and 26% respectively.

Occurrence and Distribution of Pharmaceuticals and Their Transformation Products in Luxembourgish Surface Waters

Pharmaceuticals and their transformation products (TPs) are continuously released into the aquatic environment via anthropogenic activity. To expand knowledge on the presence of pharmaceuticals and their known TPs in Luxembourgish rivers, 92 samples collected during routine monitoring events between 2019 and 2020 were investigated using nontarget analysis. Water samples were concentrated using solid-phase extraction and then analyzed using liquid chromatography coupled to a high-resolution mass spectrometer. Suspect screening was performed using several open source computational tools and resources including Shinyscreen (https://git-r3lab.uni.lu/eci/shinyscreen/), MetFrag (https://msbi.ipb-halle.de/MetFrag/), PubChemLite (https://zenodo.org/record/4432124), and MassBank (https://massbank.eu/MassBank/). A total of 94 pharmaceuticals, 88 confirmed at a level 1 confidence (86 of which could be quantified, two compounds too low to be quantified) and six identified at level 2a, were found to be present in Luxembourg rivers. Pharmaceutical TPs (12) were also found at a level 2a confidence. The pharmaceuticals were present at median concentrations up to 214 ng/L, with caffeine having a median concentration of 1424 ng/L. Antihypertensive drugs (15), psychoactive drugs (15), and antimicrobials (eight) were the most detected groups of pharmaceuticals. A spatiotemporal analysis of the data revealed areas with higher concentrations of the pharmaceuticals, as well as differences in pharmaceutical concentrations between 2019 and 2020. The results of this work will help guide activities for improving water management in the country and set baseline data for continuous monitoring and screening efforts, as well as for further open data and software developments.

Multilaboratory Collaborative Study of a Nontarget Data Acquisition for Target Analysis (nDATA) Workflow Using Liquid Chromatography-High-Resolution Accurate Mass Spectrometry for Pesticide Screening in Fruits and Vegetables

Nontarget data acquisition for target analysis (nDATA) workflows using liquid chromatography-high-resolution accurate mass (LC-HRAM) spectrometry, spectral screening software, and a compound database have generated interest because of their potential for screening of pesticides in foods. However, these procedures and particularly the instrument processing software need to be thoroughly evaluated before implementation in routine analysis. In this work, 25 laboratories participated in a collaborative study to evaluate an nDATA workflow on high moisture produce (apple, banana, broccoli, carrot, grape, lettuce, orange, potato, strawberry, and tomato). Samples were extracted in each laboratory by quick, easy, cheap, effective, rugged, and safe (QuEChERS), and data were acquired by ultrahigh-performance liquid chromatography (UHPLC) coupled to a high-resolution quadrupole Orbitrap (QOrbitrap) or quadrupole time-of-flight (QTOF) mass spectrometer operating in full-scan mass spectrometry (MS) data-independent tandem mass spectrometry (LC-FS MS/DIA MS/MS) acquisition mode. The nDATA workflow was evaluated using a restricted compound database with 51 pesticides and vendor processing software. Pesticide identifications were determined by retention time (t_R, ±0.5 min relative to the reference retention times used in the compound database) and mass errors (δ_M) of the precursor (RTP, δ_M ≤ ±5 ppm) and product ions (RTPI, δ_M ≤ ±10 ppm). The elution profiles of all 51 pesticides were within ±0.5 min among 24 of the participating laboratories. Successful screening was determined by false positive and false negative rates of <5% in unfortified (pesticide-free) and fortified (10 and 100 μg/kg) produce matrices. Pesticide responses were dependent on the pesticide, matrix, and instrument. The false negative rates were 0.7 and 0.1% at 10 and 100 μg/kg, respectively, and the false positive rate was 1.1% from results of the participating LC-HRAM platforms. Further evaluation was achieved by providing produce samples spiked with pesticides at concentrations blinded to the laboratories. Twenty-two of the 25 laboratories were successful in identifying all fortified pesticides (0-7 pesticides ranging from 5 to 50 μg/kg) for each produce sample (99.7% detection rate). These studies provide convincing evidence that the nDATA comprehensive approach broadens the screening capabilities of pesticide analyses and provide a platform with the potential to be easily extended to a larger number of other chemical residues and contaminants in foods.

Evaluation, optimization, and application of three independent suspect screening workflows for the characterization of PFASs in water

Suspect screening is a valuable tool for characterizing per- and polyfluoroalkyl substances (PFASs) in environmental media. Although a variety of data mining tools have been developed and applied for suspect screening of PFAS, few suspect screening workflows have undergone a comprehensive performance evaluation or optimization. The goals of this research were to: (1) evaluate and optimize three independent suspect screening workflows for the detection of PFASs in water samples; and (2) apply the optimized suspect screening workflows to an environmental sample to determine the extent to which suspect screening results converge. We evaluated and optimized suspect screening workflows using Compound Discoverer v3.2, enviMass v4.2, and FluoroMatch v2.4 using test samples containing 33 target PFASs. The average sensitivity (Sen) and selectivity (Sel) for each workflow across the test samples was: Compound Discoverer Sen = 71%, Sel = 85%; enviMass Sen = 89%, Sel = 80%; FluoroMatch Sen = 51%, Sel = 82%. We then applied the optimized workflows to a contaminated groundwater sample containing an unknown number of PFASs. Each workflow managed to annotate unique PFASs that were not annotated by the other workflows including 2 by Compound Discoverer and 19 each by enviMass and FluoroMatch. Thirty-two enviMass hits and 28 of the Compound Discoverer and FluoroMatch hits were annotated by at least one of the other workflows. Sixteen PFASs were annotated by all three of the optimized workflows. This work provides a basis for conducting suspect screening for PFASs that will lead to more consistent reporting of suspect screening data.

Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

Background

Non-target screening consists in searching a sample for all present substances, suspected or unknown, with very little prior knowledge about the sample. This approach has been introduced more than a decade ago in the field of water analysis, together with dedicated compound identification tools, but is still very scarce for indoor and atmospheric trace gas measurements, despite the clear need for a better understanding of the atmospheric trace gas composition. For a systematic detection of emerging trace gases in the atmosphere, a new and powerful analytical method is gas chromatography (GC) of preconcentrated samples, followed by electron ionisation, high resolution mass spectrometry (EI-HRMS). In this work, we present data analysis tools to enable automated fragment formula annotation for unknown compounds measured by GC-EI-HRMS.

Results

Based on co-eluting mass/charge fragments, we developed an innovative data analysis method to reliably reconstruct the chemical formulae of the fragments, using efficient combinatorics and graph theory. The method does not require the presence of the molecular ion, which is absent in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}∼40% of EI spectra. Our method has been trained and validated on >50 halocarbons and hydrocarbons, with 3–20 atoms and molar masses of 30–330 g mol\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{-1}$$\end{document}-1, measured with a mass resolution of approx. 3500. For >90% of the compounds, more than 90% of the annotated fragment formulae are correct. Cases of wrong identification can be attributed to the scarcity of detected fragments per compound or the lack of isotopic constraint (no minor isotopocule detected).

Conclusions

Our method enables to reconstruct most probable chemical formulae independently from spectral databases. Therefore, it demonstrates the suitability of EI-HRMS data for non-target analysis and paves the way for the identification of substances for which no EI mass spectrum is registered in databases. We illustrate the performances of our method for atmospheric trace gases and suggest that it may be well suited for many other types of samples. The L-GPL licenced Python code is released under the name ALPINAC for ALgorithmic Process for Identification of Non-targeted Atmospheric Compounds.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13321-021-00544-w.

Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.

Measurement(s)	chemical • drinking water
Technology Type(s)	high resolution mass spectrometry • non-target analysis • Interlaboratory
Factor Type(s)	method
Sample Characteristic - Environment	laboratory environment

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15028665

Identification of Pesticide Transformation Products in Surface Water Using Suspect Screening Combined with National Monitoring Data

Pesticides are widespread anthropogenic chemicals and well-known environmental contaminants of concern. Much less is known about transformation products (TPs) of pesticides and their presence in the environment. We developed a novel suspect screening approach for not well-explored pesticides (n = 16) and pesticide TPs (n = 242) by integrating knowledge from national monitoring with high-resolution mass spectrometry data. Weekly time-integrated samples were collected in two Swedish agricultural streams using the novel Time-Integrating, MicroFlow, In-line Extraction (TIMFIE) sampler. The integration of national monitoring data in the screening approach increased the number of prioritized compounds approximately twofold (from 23 to 42). Ultimately, 11 pesticide TPs were confirmed by reference standards and 12 TPs were considered tentatively identified with varying levels of confidence. Semiquantification of the newly confirmed TPs indicated higher concentrations than their corresponding parent pesticides in some cases, which highlights concerns related to (unknown) pesticide TPs in the environment. Some TPs were present in the environment without co-occurrence of their corresponding parent compounds, indicating higher persistency or mobility of the identified TPs. This study showcased the benefits of integrating monitoring knowledge in this type of studies, with advantages for suspect screening performance and the possibility to increase relevance of future monitoring programs.

An annotation database for chemicals of emerging concern in exposome research

BACKGROUND

Chemicals of Emerging Concern (CECs) include a very wide group of chemicals that are suspected to be responsible for adverse effects on health, but for which very limited information is available. Chromatographic techniques coupled with high-resolution mass spectrometry (HRMS) can be used for non-targeted screening and detection of CECs, by using comprehensive annotation databases. Establishing a database focused on the annotation of CECs in human samples will provide new insight into the distribution and extent of exposures to a wide range of CECs in humans.

OBJECTIVES

This study describes an approach for the aggregation and curation of an annotation database (CECscreen) for the identification of CECs in human biological samples.

METHODS

The approach consists of three main parts. First, CECs compound lists from various sources were aggregated and duplications and inorganic compounds were removed. Subsequently, the list was curated by standardization of structures to create "MS-ready" and "QSAR-ready" SMILES, as well as calculation of exact masses (monoisotopic and adducts) and molecular formulas. The second step included the simulation of Phase I metabolites. The third and final step included the calculation of QSAR predictions related to physicochemical properties, environmental fate, toxicity and Absorption, Distribution, Metabolism, Excretion (ADME) processes and the retrieval of information from the US EPA CompTox Chemicals Dashboard.

RESULTS

All CECscreen database and property files are publicly available (DOI: https://doi.org/10.5281/zenodo.3956586). In total, 145,284 entries were aggregated from various CECs data sources. After elimination of duplicates and curation, the pipeline produced 70,397 unique "MS-ready" structures and 66,071 unique QSAR-ready structures, corresponding with 69,526 CAS numbers. Simulation of Phase I metabolites resulted in 306,279 unique metabolites. QSAR predictions could be performed for 64,684 of the QSAR-ready structures, whereas information was retrieved from the CompTox Chemicals Dashboard for 59,739 CAS numbers out of 69,526 inquiries. CECscreen is incorporated in the in silico fragmentation approach MetFrag.

DISCUSSION

The CECscreen database can be used to prioritize annotation of CECs measured in non-targeted HRMS, facilitating the large-scale detection of CECs in human samples for exposome research. Large-scale detection of CECs can be further improved by integrating the present database with resources that contain CECs (metabolites) and meta-data measurements, further expansion towards in silico and experimental (e.g., MassBank) generation of MS/MS spectra, and development of bioinformatics approaches capable of using correlation patterns in the measured chemical features.

A new RP-HPLC method as an auxiliary tool for optimization of sample preparation procedures for tracing of PPCPs of different hydrophilicities

Recently, pharmaceutical and personal care products (PPCPs) have received considerable attention because of their increasing use. Analysis of PPCPs presents a significant analytical challenge, with high-performance liquid chromatography (HPLC) in reversed-phase mode, as the most widely used analytical technique. To facilitate the optimization of the procedures that are applied in the early stages of sample preparation, a simple and fast HPLC method is proposed in this work for the separation of some PPCPs with a wide range of hydrophilicity. Two columns were evaluated (Atlantis dC18 and Discovery HS F5); as for mobile phases: a formate buffer (40 mmol L-1, pH 4) and methanol were tested in a gradient mode. The fluorinated column allowed better separation in a shorter time and better resolution for all analytes (Rs > 1). The proposed method delivered good performance for the tracing of PPCPs and is a suitable alternative to traditional C18-based HPLC methods.

Retrospective mass spectrometric analysis of wastewater-fed mesocosms to assess the degradation of drugs and their human metabolites

Temporary rivers become dependent on wastewater effluent for base flows, which severely impacts river ecosystems through exposure to elevated levels of nutrients, dissolved organic matter, and organic micropollutants. However, biodegradation processes occurring in these rivers can be enhanced by wastewater bacteria/biofilms. Here, we evaluated the attenuation of pharmaceuticals and their human metabolites performing retrospective analysis of 120 compounds (drugs, their metabolites and transformation products) in mesocosm channels loaded with wastewater effluents twice a week for a period of 31 days. Eighteen human metabolites and seven biotransformation products were identified with high level of confidence. Compounds were classified into five categories. Type-A: recalcitrant drugs and metabolites (diclofenac, carbamazepine and venlafaxine); Type-B: degradable drugs forming transformation products (TPs) (atenolol, sitagliptin, and valsartan); Type-C: drugs for which no known human metabolites or TPs were detected (atorvastatin, azithromycin, citalopram, clarithromycin, diltiazem, eprosartan, fluconazole, ketoprofen, lamotrigine, lormetazepam, metformin, telmisartan, and trimethoprim); Type-D: recalcitrant drug metabolites (4-hydroxy omeprazole sulfide, erythro/threo-hydrobupropion, and zolpidem carboxylic acid); Type-E: unstable metabolites whose parent drug was not detectable (norcocaine, benzolylecgonine, and erythromycin A enol ether). Noteworthy was the valsartan acid formation from valsartan with transient formation of TP-336.

Non-target environmental analysis by liquid chromatography/high-resolution mass spectrometry with a product ion and neutral loss database

Despite the increasing detection of emerging substances in the environment, the identity of most are left unknown due to the lack of efficient identification methods. We developed a non-target analysis method for identifying unknown substances in the environment by liquid chromatography/high-resolution mass spectrometry (LC/HRMS) with a product ion and neutral loss database (PNDB). The present analysis describes an elucidation method with elemental compositions of the molecules, product ions, and corresponding neutral losses of the unknown substance: (1) with the molecular formula, possible molecular structures are retrieved from two chemical structure databases (PubChem and ChemSpider); then (2) with the elemental compositions of product ions and neutral losses, possible partial structures are retrieved from the PNDB; and finally, (3) molecular structures that match the possible partial structures are listed in order of number of hits. A molecular structure with a higher number of hits is more similar to the structure of the analyzed substance. The performance of the non-target method was evaluated by simulated analysis of 150 LC/HRMS spectra registered in MassBank. First, all substances of the same mass data (41/41) and 68% (39/57) of the mass data of the same substances not registered in the PNDB were elucidated. It was demonstrated that 14% (7/52) and 31% (16/52) of the substances with no mass spectral data registered in the PNDB were obtained at the first and within the fifth place, respectively. Owing to the fact that 10 of the total hits occurred in product ions and neutral losses, almost 50% of the substances evaluated with this method were placed at the top 4 positions in the similarity ranking. Importantly, the proposed method is effective for analyzing mass spectral data that has not been registered in the PNDB and thus is expected to be used for a variety of non-target analyses.

Retrospective HRMS Screening and Dedicated Target Analysis Reveal a Wide Exposure to Pyrrolizidine Alkaloids in Small Streams

Pyrrolizidine alkaloids (PAs) are found to be toxic pollutants emitted into the environment by numerous plant species, resulting in contamination. In this article, we investigate the occurrence of PAs in the aquatic environment of small Swiss streams combining two different approaches. Pyrrolizidine alkaloids (PAs) are toxic secondary metabolites produced by numerous plant species. Although they were classified as persistent and mobile and found to be emitted into the environment, their occurrence in surface waters is largely unknown. Therefore, we performed a retrospective data analysis of two extensive HRMS campaigns each covering five small streams in Switzerland over the growing season. All sites were contaminated with up to 12 individual PAs and temporal detection frequencies between 36 and 87%. Individual PAs were in the low ng/L range, but rain-induced maximal total PA concentrations reached almost 100 ng/L in late spring and summer. Through PA patterns in water and plants, several species were tentatively identified as the source of contamination, with Senecio spp. and Echium vulgare being the most important. Additionally, two streams were monitored, and PAs were quantified with a newly developed, faster, and more sensitive LC-MS/MS method to distinguish different plant-based and indirect human PA sources. A distinctly different PA fingerprint in aqueous plant extracts pointed to invasive Senecio inaequidens as the main source of the surface water contamination at these sites. Results indicate that PA loads may increase if invasive species are sufficiently abundant.

New methods for identification of disinfection byproducts of toxicological relevance: Progress and future directions

Disinfection byproducts (DBPs) represent a ubiquitous source of chemical exposure in disinfected water. While over 700 DBPs have been identified, the drivers of toxicity remain poorly understood. Additionally, ever evolving water treatment practices have led to a continually growing list of DBPs. Advancement of analytical technologies have enabled the identification of new classes of DBPs and the quantification of these chemically diverse sets of DBPs. Here we summarize advances in new workflows for DBP analysis, including sample preparation, chromatographic separation with mass spectrometry (MS) detection, and data processing. To aid in the selection of techniques for future studies, we discuss necessary considerations for each step in the strategy. This review focuses on how each step of a workflow can be optimized to capture diverse classes of DBPs within a single method. Additionally, we highlight new MS-based approaches that can be powerful for identifying novel DBPs of toxicological relevance. We discuss current challenges and provide perspectives on future research directions with respect to studying new DBPs of toxicological relevance. As analytical technologies continue to advance, new strategies will be increasingly used to analyze complex DBPs produced in different treatment processes with the aim to identify potential drivers of toxicity.

Improving Target and Suspect Screening High-Resolution Mass Spectrometry Workflows in Environmental Analysis by Ion Mobility Separation

Currently, the most powerful approach to monitor organic micropollutants (OMPs) in environmental samples is the combination of target, suspect, and nontarget screening strategies using high-resolution mass spectrometry (HRMS). However, the high complexity of sample matrices and the huge number of OMPs potentially present in samples at low concentrations pose an analytical challenge. Ion mobility separation (IMS) combined with HRMS instruments (IMS-HRMS) introduces an additional analytical dimension, providing extra information, which facilitates the identification of OMPs. The collision cross-section (CCS) value provided by IMS is unaffected by the matrix or chromatographic separation. Consequently, the creation of CCS databases and the inclusion of ion mobility within identification criteria are of high interest for an enhanced and robust screening strategy. In this work, a CCS library for IMS-HRMS, which is online and freely available, was developed for 556 OMPs in both positive and negative ionization modes using electrospray ionization. The inclusion of ion mobility data in widely adopted confidence levels for identification in environmental reporting is discussed. Illustrative examples of OMPs found in environmental samples are presented to highlight the potential of IMS-HRMS and to demonstrate the additional value of CCS data in various screening strategies.

Environmental effects of offshore produced water discharges: A review focused on the Norwegian continental shelf

Produced water (PW), a large byproduct of offshore oil and gas extraction, is reinjected to formations or discharged to the sea after treatment. The discharges contain dispersed crude oil, polycyclic aromatic hydrocarbons (PAHs), alkylphenols (APs), metals, and many other constituents of environmental relevance. Risk-based regulation, greener offshore chemicals and improved cleaning systems have reduced environmental risks of PW discharges, but PW is still the largest operational source of oil pollution to the sea from the offshore petroleum industry. Monitoring surveys find detectable exposures in caged mussel and fish several km downstream from PW outfalls, but biomarkers indicate only mild acute effects in these sentinels. On the other hand, increased concentrations of DNA adducts are found repeatedly in benthic fish populations, especially in haddock. It is uncertain whether increased adducts could be a long-term effect of sediment contamination due to ongoing PW discharges, or earlier discharges of oil-containing drilling waste. Another concern is uncertainty regarding the possible effect of PW discharges in the sub-Arctic Southern Barents Sea. So far, research suggests that sub-arctic species are largely comparable to temperate species in their sensitivity to PW exposure. Larval deformities and cardiac toxicity in fish early life stages are among the biomarkers and adverse outcome pathways that currently receive much attention in PW effect research. Herein, we summarize the accumulated ecotoxicological knowledge of offshore PW discharges and highlight some key remaining knowledge needs.

Untargeted Screening in a Case Control Study Using Apples as a Matrix

Untargeted screening using high resolution mass spectrometry (HRMS) is a promising approach for screening the food supply for contaminants, but the sheer amount of information inherent to the HRMS data set presents analytical challenges. Red apples, collected during the U.S. FDA's Total Diet Study, were studied to determine whether bioinformatic software can be used to distinguish spiked model compounds from those native to apples. A workflow was created, in which initial data sets of over 44,000 features in each of the two spiked samples were reduced by several orders of magnitude to a scale suitable for visual inspection. After visual inspection to address degeneracy and data quality, the final data sets contained 30 and 2 suspect compounds, respectively. To the best of our knowledge, this is the largest scale case-control study on food matrices to date and the first use of market basket samples as references in an untargeted screening study.

Perspectives and challenges associated with the determination of new psychoactive substances in urine and wastewater - A tutorial

New psychoactive substances (NPS), often designed as (legal) substitutes to conventional illicit drugs, are constantly emerging in the drug market and being commercialized in different ways and forms. Their use continues to cause public health problems and is therefore of major concern in many countries. Monitoring NPS use, however, is arduous and different sources of information are required to get more insight of the prevalence and diffusion of NPS use. The determination of NPS in pooled urine and wastewater has shown great potential, adding a different and complementary light on this issue. However, it also presents analytical challenges and limitations that must be taken into account such as the complexity of the matrices, the high sensitivity and selectivity required in the analytical methods as a consequence of the low analyte concentrations as well as the rapid transience of NPS on the drug market creating a scenario with constantly moving analytical targets. Analytical investigation of NPS in pooled urine and wastewater is based on liquid chromatography hyphenated to mass spectrometry and can follow different strategies: target, suspect and non-target analysis. This work aims to discuss the advantages and disadvantages of the different data acquisition workflows and data exploration approaches in mass spectrometry, but also pays attention to new developments such as ion mobility and the use of in-silico prediction tools to improve the identification capabilities in high-complex samples. This tutorial gives an insight into this emerging topic of current concern, and describes the experience gathered within different collaborations and projects supported by key research articles and illustrative practical examples.